NB: this is on development process, many things we want to develop but have not yet done. If you want to contribute please feel free to do so. We are according to nbdev style. So if you do contribute, please do so accordingly. For more information about nbdev style, please visit nbdev document
git clone https://github.com/KienVu2368/tabint
cd tabint
conda env create -f environment.yml
conda activate tabintimport pandas as pd
df = pd.read_csv('df_sample.csv')
df_proc, y, pp_outp = tabular_proc(df, 'TARGET', [fill_na(), app_cat(), dummies()])Unify class for pre processing class.
class cls(TBPreProc):
@staticmethod
def func(df, pp_outp, na_dict = None):
...
return dfFor example, fill_na class
class fill_na(TBPreProc):
@staticmethod
def func(df, pp_outp, na_dict = None):
na_dict = {} if na_dict is None else na_dict.copy()
na_dict_initial = na_dict.copy()
for n,c in df.items(): na_dict = fix_missing(df, c, n, na_dict)
if len(na_dict_initial.keys()) > 0:
df.drop([a '_na' for a in list(set(na_dict.keys()) - set(na_dict_initial.keys()))], axis=1, inplace=True)
pp_outp['na_dict'] = na_dict
return dfDataset class contain training set, validation set and test set.
Dataset can be built by split method of SKlearn
ds = TBDataset.from_SKSplit(df_proc, y, cons, cats, ratio = 0.2)Or by split method of tabint. This method will try to keep the same distribution of categorie variables between training set and validation set.
ds = TBDataset.from_TBSplit(df_proc, y, cons, cats, ratio = 0.2)Dataset class have method that can simultaneously edit training set, validation set and test set.
Drop method can drop one or many columns in training set, validation set and test set.
ds.drop('DAYS_LAST_PHONE_CHANGE_na')Or if we need to keep only importance columns that we found above. Just use keep method from dataset.
mpt_features = impt.top_features(24)
ds.keep(impt_features)Dataset class in tabint also can simultaneously apply a funciton to training set, validation set and test set
ds.apply('DAYS_BIRTH', lambda df: -df['DAYS_BIRTH']/365)Or we can pass many transformation function at once.
tfs = {'drop 1': ['AMT_REQ_CREDIT_BUREAU_HOUR_na', 'AMT_REQ_CREDIT_BUREAU_YEAR_na'],
'apply':{'DAYS_BIRTH': lambda df: -df['DAYS_BIRTH']/365,
'DAYS_EMPLOYED': lambda df: -df['DAYS_EMPLOYED']/365,
'NEW_EXT_SOURCES_MEAN': lambda df: df[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']].mean(axis=1, skipna=True),
'NEW_EXT_SOURCES_GEO': lambda df: (df['EXT_SOURCE_1']*df['EXT_SOURCE_2']*df['EXT_SOURCE_3'])**(1/3),
'AMT_CREDIT/AMT_GOODS_PRICE': lambda df: df['AMT_CREDIT']/df['AMT_GOODS_PRICE'],
'AMT_CREDIT/AMT_CREDIT': lambda df: df['AMT_CREDIT']/df['AMT_CREDIT'],
'DAYS_EMPLOYED/DAYS_BIRTH': lambda df: df['DAYS_EMPLOYED']/df['DAYS_BIRTH'],
'DAYS_BIRTH*EXT_SOURCE_1_na': lambda df: df['DAYS_BIRTH']*df['EXT_SOURCE_1_na']},
'drop 2': ['AMT_ANNUITY', 'AMT_CREDIT', 'AMT_GOODS_PRICE']}
ds.transform(tfs)Learner class unify training method from sklearn model
learner = LGBLearner()
params = {'task': 'train', 'objective': 'binary', 'metric':'binary_logloss'}
learner.fit(params, *ds.trn, *ds.val)LGBM model
learner = SKLearner(RandomForestClassifier())
learner.fit(*ds.trn, *ds.val)and XGB model (WIP)
tabint use đenogram for easy to see and pick features with high correlation
ddg = Dendogram.from_df(ds.x_trn)ddg.plot()tabint use permutation importance. Each column or group of columns in validation set in dataset will be permute to calculate the importance.
group_cols = [['AMT_CREDIT', 'AMT_GOODS_PRICE', 'AMT_ANNUITY'], ['FLAG_OWN_CAR_N', 'OWN_CAR_AGE_na']]impt = Importance.from_Learner(learner, ds, group_cols)impt.plot()We can easily get the most importance feature by method in Importance class
impt.top_features(24)roc = ReceiverOperatingCharacteristic.from_learner(learner, ds)
roc.plot()kde = KernelDensityEstimation.from_learner(learner, ds)
kde.plot()pr = PrecisionRecall.from_series(y_true, y_pred)
pr.plot()avp = actual_vs_predict.from_learner(learner, ds)
avp.plot(hue = 'Height')tabint use PDPbox library to visualize partial dependence.
pdp = PartialDependence.from_Learner(learner, ds)pdp.info_target_plot('EXT_SOURCE_3')We can see result as table
pdp.info_target_data()pdp.isolate_plot('EXT_SOURCE_3')Tf = Traterfall.from_SKTree(learner, ds.x_trn, 3)Tf.plot(formatting = "$ {:,.3f}")We can see and filter result table
Tf.data.pos(5)
Tf.data.neg(5)tabint visual SHAP values from SHAP library. SHAP library use red and blue for default color. tabint change these color to green and blue for easy to see and consistence with pdpbox library.
Shap = SHAP.from_Tree(learner, ds)Shap.one_force_plot(3)And we can see table result also.
Shap.one_force_data.pos(5)Shap.one_force_data.neg(5)Shap.dependence_plot('EXT_SOURCE_2')